ArticleGraefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie2026
Surgical outcomes and risk factors identification for rhegmatogenous retinal detachment repair by pneumatic retinopexy using pure air.
Article in Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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8 authors.
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Abstract
objectiveThis study aimed to evaluate the clinical outcomes and identify risk factors for failure of pneumatic retinopexy (PR) using pure air.
methodsA retrospective analysis was conducted on 199 eyes from 199 patients with uncomplicated RRD who underwent PR at a single institution between 2019 and 2024. All patients were followed for at least 6 months, with specific follow-up intervals at 1 week and 1, 3, and 6 months. The primary outcome was the anatomical success rate. Statistical analyses, including univariate and multivariate logistic regression, were performed to identify preoperative risk factors for surgical failure. A machine learning model (Random Forest) was also developed to predict surgical success.
resultsThe single-operation anatomical success rate was 83.9% (167/199). Following repeated gas injection in select cases, the final success rate increased to 89.4% (178/199). Multivariate analysis identified younger age (< 50 years) (Odds Ratio [OR] 2.66; P < 0.05), the presence of a round hole (as opposed to a horseshoe tear) (OR 2.35; P < 0.05) as independent risk factors for primary PR failure. Additionally, lattice degeneration in the attached retina emerged as a potential risk factor with borderline statistical significance (OR 2.49, P = 0.046). A Random Forest model employed for predicting surgical success yielded favorable outcomes, with an accuracy of 0.88 and an AUC of 0.819.
conclusionPneumatic retinopexy using intravitreal air is an effective treatment for uncomplicated RRD, achieving high final anatomical success rates. However, surgeons should be aware of a higher risk of failure in younger patients and in eyes with round retinal holes or lattice degeneration in the attached retina. Machine learning models show promise for preoperatively predicting surgical outcomes.
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